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Nine glycolysis-related gene signature predicting the survival of patients with endometrial adenocarcinoma
BACKGROUND: Endometrial cancer is the fourth most common cancer in women. The death rate for endometrial cancer has increased. Glycolysis of cellular respiration is a complex reaction and is the first step in most carbohydrate catabolism, which was proved to participate in tumors. METHODS: We analyz...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7247270/ https://www.ncbi.nlm.nih.gov/pubmed/32489319 http://dx.doi.org/10.1186/s12935-020-01264-1 |
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author | Liu, JinHui Li, SiYue Feng, Gao Meng, HuangYang Nie, SiPei Sun, Rui Yang, Jing Cheng, WenJun |
author_facet | Liu, JinHui Li, SiYue Feng, Gao Meng, HuangYang Nie, SiPei Sun, Rui Yang, Jing Cheng, WenJun |
author_sort | Liu, JinHui |
collection | PubMed |
description | BACKGROUND: Endometrial cancer is the fourth most common cancer in women. The death rate for endometrial cancer has increased. Glycolysis of cellular respiration is a complex reaction and is the first step in most carbohydrate catabolism, which was proved to participate in tumors. METHODS: We analyzed the sample data of over 500 patients from TCGA database. The bioinformatic analysis included GSEA, cox and lasso regression analysis to select prognostic genes, as well as construction of a prognostic model and a nomogram for OS evaluation. The immunohistochemistry staining, survival analysis and expression level validation were also performed. Maftools package was for mutation analysis. GSEA identified Glycolysis was the most related pathway to EC. qRT-PCR verified the expression level of hub gene in clinical samples. RESULTS: According to the prognostic model using the train set, 9 glycolysis-related genes including B3GALT6, PAM, LCT, GMPPB, GLCE, DCN, CAPN5, GYS2 and FBP2 were identified as prognosis-related genes. Based on nine gene signature, the EC patients could be classified into high and low risk subgroups, and patients with high risk score showed shorter survival time. Time-dependent ROC analysis and Cox regression suggested that the risk score predicted EC prognosis accurately and independently. Analysis of test and train sets yielded consistent results A nomogram which incorporated the 9-mRNA signature and clinical features was also built for prognostic prediction. Immunohistochemistry staining and TCGA validation showed that expression levels of these genes do differ between EC and normal tissue samples. GSEA revealed that the samples of the low-risk group were mainly concentrated on Bile Acid Metabolism. Patients in the low-risk group displayed obvious mutation signatures compared with those in the high-risk group. The expression levels of B3GALT6, DCN, FBP2 and GYS2 are lower in tumor samples and higher in normal tissue samples. The expression of CAPN5 and LCT in clinical sample tissues is just the opposite. CONCLUSION: This study found that the Glycolysis pathway is associated with EC and screened for hub genes on the Glycolysis pathway, which may serve as new target for the treatment of EC. |
format | Online Article Text |
id | pubmed-7247270 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-72472702020-06-01 Nine glycolysis-related gene signature predicting the survival of patients with endometrial adenocarcinoma Liu, JinHui Li, SiYue Feng, Gao Meng, HuangYang Nie, SiPei Sun, Rui Yang, Jing Cheng, WenJun Cancer Cell Int Primary Research BACKGROUND: Endometrial cancer is the fourth most common cancer in women. The death rate for endometrial cancer has increased. Glycolysis of cellular respiration is a complex reaction and is the first step in most carbohydrate catabolism, which was proved to participate in tumors. METHODS: We analyzed the sample data of over 500 patients from TCGA database. The bioinformatic analysis included GSEA, cox and lasso regression analysis to select prognostic genes, as well as construction of a prognostic model and a nomogram for OS evaluation. The immunohistochemistry staining, survival analysis and expression level validation were also performed. Maftools package was for mutation analysis. GSEA identified Glycolysis was the most related pathway to EC. qRT-PCR verified the expression level of hub gene in clinical samples. RESULTS: According to the prognostic model using the train set, 9 glycolysis-related genes including B3GALT6, PAM, LCT, GMPPB, GLCE, DCN, CAPN5, GYS2 and FBP2 were identified as prognosis-related genes. Based on nine gene signature, the EC patients could be classified into high and low risk subgroups, and patients with high risk score showed shorter survival time. Time-dependent ROC analysis and Cox regression suggested that the risk score predicted EC prognosis accurately and independently. Analysis of test and train sets yielded consistent results A nomogram which incorporated the 9-mRNA signature and clinical features was also built for prognostic prediction. Immunohistochemistry staining and TCGA validation showed that expression levels of these genes do differ between EC and normal tissue samples. GSEA revealed that the samples of the low-risk group were mainly concentrated on Bile Acid Metabolism. Patients in the low-risk group displayed obvious mutation signatures compared with those in the high-risk group. The expression levels of B3GALT6, DCN, FBP2 and GYS2 are lower in tumor samples and higher in normal tissue samples. The expression of CAPN5 and LCT in clinical sample tissues is just the opposite. CONCLUSION: This study found that the Glycolysis pathway is associated with EC and screened for hub genes on the Glycolysis pathway, which may serve as new target for the treatment of EC. BioMed Central 2020-05-24 /pmc/articles/PMC7247270/ /pubmed/32489319 http://dx.doi.org/10.1186/s12935-020-01264-1 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Primary Research Liu, JinHui Li, SiYue Feng, Gao Meng, HuangYang Nie, SiPei Sun, Rui Yang, Jing Cheng, WenJun Nine glycolysis-related gene signature predicting the survival of patients with endometrial adenocarcinoma |
title | Nine glycolysis-related gene signature predicting the survival of patients with endometrial adenocarcinoma |
title_full | Nine glycolysis-related gene signature predicting the survival of patients with endometrial adenocarcinoma |
title_fullStr | Nine glycolysis-related gene signature predicting the survival of patients with endometrial adenocarcinoma |
title_full_unstemmed | Nine glycolysis-related gene signature predicting the survival of patients with endometrial adenocarcinoma |
title_short | Nine glycolysis-related gene signature predicting the survival of patients with endometrial adenocarcinoma |
title_sort | nine glycolysis-related gene signature predicting the survival of patients with endometrial adenocarcinoma |
topic | Primary Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7247270/ https://www.ncbi.nlm.nih.gov/pubmed/32489319 http://dx.doi.org/10.1186/s12935-020-01264-1 |
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